Method and device for improving Hartmann wavefront measurement precision based on time sequence correlation characteristics

Abstract

The invention provides a method and device for improving Hartmann wavefront measurement precision based on time sequence correlation characteristics, and belongs to the technical field of optical aberration measurement. According to the method, the time sequence correlation between a detection signal and a reference signal of each pixel point is calculated, an appropriate time sequence correlation threshold value is selected, the pixel points with high signal-to-noise ratios are screened to select light spot areas participating in centroid calculation, and the selected light spot areas are used to calculate the light spot centroid. According to the method, limitation in threshold selection and noise suppression can be avoided, the sensitivity degree of wavefront measurement precision to noise is reduced, and the wavefront detection precision and stability of the Hartmann wavefront sensor in the field of high-precision measurement are further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of optical aberration measurement, and in particular relates to a method and a device for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics. Background Art

[0002] As a real-time wavefront measurement device with simple structure and high measurement accuracy, the Hartmann wavefront sensor is widely used in many high-precision wavefront detection fields such as astronomy, adaptive optics, ophthalmology, laser detection, etc. The Hartmann wavefront sensor consists of a microlens array and a detector. The microlens array divides the incident wavefront into several sub-wavefronts, which are focused on the detector to form a spot array image. The position change of the sub-spot in the spot array image reflects the slope information of the wavefront in the corresponding sub-aperture. Therefore, the phase information of the entire wavefront can be restored through the centroid position offset of each sub-spot, realizing high-precision wavefront detection.

[0003] The accuracy of the spot centroid position calculation is a key factor affecting the accuracy of Hartmann wavefront measurement. The traditional spot centroid algorithm has limitations in threshold selection, and its rationality needs to be improved. In addition, the traditional centroid algorithm has low measurement accuracy and is highly sensitive to noise. In the field of high-precision wavefront measurement or low signal-to-noise ratio wavefront measurement, it cannot meet the measurement accuracy requirements. In the field of high-precision detection, noise has a significant impact on measurement results, especially the impact of photon noise on measurement results cannot be ignored. Summary of the invention

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: a method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics, comprising the following steps:

[0005] Step 1: Sine wave modulation is performed on the intensity of the light spot so that the intensity of the light spot has a periodic variation rule in time sequence;

[0006] Step 2: Take the pixel point where the maximum gray value of the sub-spot is located as the window center and L×L pixels as the window size, perform regional screening on the sub-spot image, and select the main spot area;

[0007] Step 3: pre-process the sub-spot signal;

[0008] Step 4: Calculate the mean gray value of each frame of the spot image;

[0009] Step 5: Calculate the correlation between the detection signal of each pixel and the reference signal in time series, and obtain the correlation value between the detection signal of each pixel and the reference signal in time series;

[0010] Step 6: Select the spot area involved in the centroid calculation according to the correlation value of the detection signal and the reference signal of each pixel point in time series and the time series correlation threshold;

[0011] Step 7: Using the spot area selected above, calculate the spot centroid and sub-wavefront slope.

[0012] A device for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics, comprising:

[0013] The modulation module is used to perform sinusoidal modulation on the intensity of the light spot so that the intensity of the light spot has a periodic variation rule in time sequence;

[0014] The area screening module is used to take the pixel point where the maximum gray value of the sub-spot is located as the window center and L×L pixels as the window size to perform area screening on the sub-spot image and select the main spot area;

[0015] A preprocessing module, used for preprocessing the sub-spot signal;

[0016] A mean value calculation module is used to calculate the mean value of the grayscale value of each frame of the spot image;

[0017] A time series correlation calculation module is used to calculate the time series correlation between the detection signal of each pixel point and the reference signal, and obtain the time series correlation value between the detection signal of each pixel point and the reference signal;

[0018] A selection module, used to select a spot area participating in centroid calculation according to a correlation value of a detection signal of each pixel point and a reference signal in time sequence and a time sequence correlation threshold;

[0019] The sub-wavefront slope calculation module is used to calculate the spot centroid and the sub-wavefront slope using the selected spot area.

[0020] An electronic device comprises: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the method described in the method.

[0021] A computer readable storage medium stores executable instructions, which, when executed by a processor, enable the processor to implement the method. A computer program product includes a computer program, which, when executed by a processor, implements the method.

[0022] The present invention has the following beneficial effects:

[0023] (1) The method of the present invention improves the measurement accuracy of Hartmann wavefront based on the time series correlation characteristics. By setting the time series correlation coefficient threshold to screen pixel points, the limitations and rationality of the threshold selection of the traditional threshold-removing centroid algorithm are overcome.

[0024] (2) The method of the present invention improves the measurement accuracy of Hartmann wavefront based on the timing correlation characteristics. By eliminating the light spot pixels with low signal-to-noise ratio, the light spot readout noise, photon noise, etc. are further suppressed, thereby reducing the influence of noise on the measurement accuracy.

[0025] (3) The method of the present invention improves the measurement accuracy of Hartmann wavefront based on the time-series correlation characteristics. By retaining the light spot pixels with high signal-to-noise ratio, the measurement results are less sensitive to noise and light intensity, and high-precision measurement results can be obtained under low signal-to-noise ratio conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a method for improving the measurement accuracy of Hartmann wavefront based on time-series correlation characteristics;

[0027] Figure 2 It is a schematic diagram of the correlation between the detection signal and the reference signal of each pixel point in the two-dimensional space in terms of time sequence;

[0028] Figure 3 This is a schematic diagram of the spot area image after the original spot image is filtered by the time-series correlation threshold. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] Figure 1 Flow chart of a method for improving the measurement accuracy of Hartmann wavefront based on time-series correlation characteristics. In a specific embodiment, a light spot in a certain sub-aperture is analyzed as an example, and the main light spot size D×D is set to 30×30 pixels. Figure 1 As shown, the method comprises the following steps:

[0031] Step 1: Sine wave modulate the light spot intensity so that the light spot intensity has a periodic change rule in time series for subsequent time series correlation analysis; the frequency of the light spot intensity modulation is adjusted according to the acquisition frame rate and acquisition time of the camera image, and the amplitude of the light spot intensity modulation is adjusted according to the camera readout noise and the size of the light spot intensity peak. The selected parameters are used to reflect the periodic change of the light spot intensity.

[0032] Step 2: Take the pixel point where the maximum gray value of the sub-spot is located as the rough estimate of the spot center, that is, the window center (the window center is set to the peak position of the spot image or the position calculated by the centroid method), use 36×36 pixels as the window size, perform regional screening on the sub-spot image, select the main spot area, and the spot image size after screening is 36×36 pixels (the window size L is selected to be 3 to 6 pixels larger than the main spot size), thereby reducing the impact of the spot diffraction sidelobe.

[0033] Step 3: Preprocess the spot signal according to formula (1) to reduce the influence of dark background and noise around the main spot;

[0034] (1)

[0035] in, is the spot image Row pixels, is the spot image Column pixels, For the The first The gray value of the detection signal of each pixel, is a single pre-set threshold.

[0036] Step 4: According to formula (2), the mean grayscale value of each frame of the spot image is calculated to further reduce the influence of noise and make the spot signal closer to the real signal. At the same time, the mean grayscale value of each frame of the spot image will be used as the reference signal for timing analysis.

[0037] (2)

[0038] in, is the mean gray value of each frame of the spot image, M is the number of pixel rows of each frame of the spot image, and each frame of the image has Row pixels, is the number of columns of pixels in each frame of the spot image. Each frame of the image has N columns of pixels. In this embodiment, and The values ​​are 36 and 36 respectively.

[0039] Step 5: According to formula (3), the Pearson correlation coefficient method is used to calculate the correlation between the detection signal and the reference signal of each pixel in time series. Figure 2 It is a schematic diagram of the correlation between the detection signal and the reference signal of each pixel point in two-dimensional space in time series, wherein the data on the right represents the correlation between the detection signal and the reference signal of each pixel point in time series.

[0040] (3)

[0041] in, is the correlation value between the detection signal and the reference signal of each pixel point in time series, is the covariance function, is the standard deviation function. Other correlation calculation methods can also be used to calculate the correlation between the detection signal of each pixel point and the reference signal in time series.

[0042] Step 6: According to formula (4), select the spot area involved in the centroid calculation. Figure 3 This is a schematic diagram of the spot area image after the original spot image is filtered by the time-series correlation threshold. The filtered spot pixel points are used to calculate the spot centroid;

[0043] (4)

[0044] in, is the timing correlation threshold. According to the simulation results and the actual measurement accuracy requirements, a suitable timing correlation threshold is selected. The timing correlation threshold of this example is 0.97.

[0045] Step 7: Using the selected spot area, calculate the spot centroid according to formulas (5) and (6).

[0046] (5)

[0047] (6)

[0048] in, is the centroid position of the spot image in the tth frame, For the Pixels The coordinates of the direction, For the Pixels The coordinates of the direction.

[0049] According to the centroid of the detection spot and the ideal spot centroid ) to get the center of mass offset of the light spot ), and the sub-wavefront slope is calculated according to formula (7).

[0050] (7)

[0051] in, and The sub-wavefronts are and The sub-wavefront slope in the direction, is the focal length of the microlens. After obtaining the sub-wavefront slope, the entire wavefront phase can be restored by the pattern restoration method. The above calculation process improves the accuracy of Hartmann wavefront measurement by improving the calculation accuracy of each sub-wavefront slope.

[0052] The method for improving the measurement accuracy of Hartmann wavefront based on the timing correlation characteristics of the present invention implemented by the above calculation process can reduce the influence of noise on wavefront measurement and improve the measurement accuracy. The basic principle is as follows:

[0053] Detection signal of each pixel point of the spot image The composition of is shown in formula (8).

[0054] (8)

[0055] in, For the The gray value of the detection signal of each pixel, For the The gray value of the real light signal of each pixel, For the The gray value of the noise signal at each pixel.

[0056] According to the components of the detection signal of the pixel point, when the real light signal accounts for the main component of the detection signal of the pixel point, the detection signal is closer to the real light signal, the correlation between the detection signal and the real light signal is greater, and the pixel point signal-to-noise ratio is higher; when the noise signal accounts for the main component of the detection signal of the pixel point, the difference between the detection signal and the real light signal is greater, the correlation between the detection signal and the real light signal is smaller, and the signal-to-noise ratio is lower. Therefore, the correlation between the detection signal of each pixel point and the real signal in time series is positively correlated with the signal-to-noise ratio, and approximately satisfies a linear relationship, as shown in formula (9).

[0057] (9)

[0058] in, For the The signal-to-noise ratio of each pixel, is the proportionality coefficient, is a constant.

[0059] by Directional spot centroid The calculation is taken as an example for analysis, as shown in formula (10). When the signal-to-noise ratio increases, the centroid of the light spot is closer to the centroid position calculated from the real light signal, and the measurement accuracy is higher.

[0060] (10)

[0061] in, For the The gray value of the detection signal of each pixel, represents the spot signal-to-noise ratio, is the centroid position calculated for the real light signal, is the centroid position calculated for the noise signal, For the The gray value of the real light signal of each pixel, For the The gray value of the noise signal at each pixel.

[0062] Therefore, the method of improving the measurement accuracy of Hartmann wavefront based on the timing correlation characteristics of the present invention is essentially to use the signal-to-noise ratio threshold to screen the pixel points of the spot detection signal, so as to retain the pixel points with high signal-to-noise ratio, so that they participate in the calculation of the spot centroid, thereby improving the measurement accuracy of the spot centroid. However, it is difficult to determine the signal-to-noise ratio threshold due to the influence of noise and light intensity. According to the composition analysis of the detection signal of the pixel point, there is a linear relationship between the signal-to-noise ratio of the pixel point and the timing correlation size. Therefore, the timing correlation threshold is used instead of the signal-to-noise ratio threshold, and the correlation size between the detection signal of each pixel point and the reference signal is analyzed in the timing, and the appropriate timing correlation threshold is selected to screen the pixel points with a high signal-to-noise ratio.

[0063] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any replacement or addition or reduction that can be understood by anyone familiar with the technology within the technical scope disclosed by the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0064] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics, characterized in that: The following steps are involved: Step 1: Sine wave modulation is performed on the intensity of the light spot so that the intensity of the light spot has a periodic variation rule in time sequence; Step 2: Take the pixel point where the maximum gray value of the sub-spot is located as the window center and L×L pixels as the window size, perform regional screening on the sub-spot image, and select the main spot area; Step 3: pre-process the sub-spot signal; Step 4: Calculate the mean gray value of each frame of the spot image; Step 5: Calculate the correlation between the detection signal of each pixel and the reference signal in time series, and obtain the correlation value between the detection signal of each pixel and the reference signal in time series; Step 6: Select the spot area involved in the centroid calculation according to the correlation value of the detection signal and the reference signal of each pixel point in time series and the time series correlation threshold; Step 7: Using the spot area selected above, calculate the spot centroid and sub-wavefront slope.

2. The method for improving the measurement accuracy of Hartmann wavefront based on the time series correlation characteristics according to claim 1, characterized in that: In step 1, the frequency of the spot intensity modulation is adjusted according to the acquisition frame rate and acquisition time of the camera image, and the amplitude of the spot intensity modulation is adjusted according to the camera readout noise and the size of the spot intensity peak. The selected parameters are used to reflect the periodic change of the spot intensity.

3. The method for improving the measurement accuracy of Hartmann wavefront based on the time series correlation characteristic according to claim 1, characterized in that: In step 2, the window size L is 3 to 6 pixels larger than the main spot size.

4. The method for improving the measurement accuracy of Hartmann wavefront based on the time series correlation characteristic according to claim 1, characterized in that: In step 2, the window center is set to the peak position of the spot image or the position calculated by the centroid method.

5. The method for improving the measurement accuracy of Hartmann wavefront based on the time series correlation characteristic according to claim 1, characterized in that: In step 3, the light spot signal is preprocessed according to formula (1); （1） in, is the spot image Row pixels, is the spot image Column pixels, For the The first The gray value of the detection signal of each pixel, is a single pre-set threshold.

6. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 1, characterized in that: In step 4, according to formula (2), the mean gray value of each frame of the spot image is calculated; （2） in, is the spot image Row pixels, is the spot image Column pixels, is the mean gray value of each frame of the spot image, M is the number of pixel rows of each frame of the spot image, and each frame of the image has Row pixels, is the number of columns of pixels in each frame of the spot image. Each frame of the image has N columns of pixels. For the The first The gray value of the detection signal of each pixel.

7. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 1, characterized in that: In step 5, a correlation calculation method is used to calculate the correlation between the detection signal of each pixel point and the reference signal in time sequence.

8. The method for improving the measurement accuracy of Hartmann wavefront based on the time series correlation characteristic according to claim 7, characterized in that: The correlation calculation method is Pearson correlation coefficient, Spearman rank correlation coefficient or Kendall rank correlation coefficient.

9. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 1, characterized in that: In step 5, according to formula (3), the Pearson correlation coefficient method is used to calculate the correlation between the detection signal and the reference signal of each pixel point in time series; （3） in, For the The first The gray value of the detection signal of each pixel, is the mean gray value of each frame of the spot image, is the correlation value between the detection signal and the reference signal of each pixel point in time series, is the covariance function, is the standard deviation function.

10. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 1, characterized in that: In step 6, according to formula (4), the spot area involved in the centroid calculation is selected, and the selected spot pixel points are used to calculate the spot centroid; （4） in, is the timing correlation threshold, For the The first The gray value of the detection signal of each pixel, is the correlation value between the detection signal and the reference signal of each pixel in time series.

11. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 10, characterized in that: The timing correlation threshold It is any value between 0 and 1 that meets the measurement accuracy requirements.

12. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 1, characterized in that: In step 7, the centroid of the light spot is calculated according to formulas (5) and (6); （5） （6） in, is the centroid position of the spot image in the tth frame, For the Pixels The coordinates of the direction, For the Pixels Coordinates of the direction; For the The first The gray value of the detection signal of each pixel point, M is the number of pixel rows of each frame of the spot image, and each frame of the image has Row pixels, is the number of columns of pixels in each frame of the spot image. Each frame of the image has N columns of pixels. is the spot image Row pixels, is the spot image Column pixels.

13. The method for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics according to claim 12, characterized in that: In step 7, According to the centroid of the detection spot and the ideal spot centroid ) to get the center of mass offset of the light spot ), and calculate the sub-wavefront slope according to formula (7); （7） in, and The sub-wavefronts are and The sub-wavefront slope in the direction, is the focal length of the microlens.

14. A device for improving the measurement accuracy of Hartmann wavefront based on time series correlation characteristics, characterized in that: include: The modulation module is used to perform sinusoidal modulation on the intensity of the light spot so that the intensity of the light spot has a periodic variation rule in time sequence; The area screening module is used to take the pixel point where the maximum gray value of the sub-spot is located as the window center and L×L pixels as the window size to perform area screening on the sub-spot image and select the main spot area; A preprocessing module, used for preprocessing the sub-spot signal; A mean value calculation module is used to calculate the mean value of the grayscale value of each frame of the spot image; A time series correlation calculation module is used to calculate the time series correlation between the detection signal of each pixel point and the reference signal, and obtain the time series correlation value between the detection signal of each pixel point and the reference signal; A selection module, used to select a spot area participating in centroid calculation according to a correlation value of a detection signal of each pixel point and a reference signal in time sequence and a time sequence correlation threshold; The sub-wavefront slope calculation module is used to calculate the spot centroid and the sub-wavefront slope using the selected spot area.

15. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by a processor, the processor implements the method according to any one of claims 1 to 13.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 13 is implemented.

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